Python DS Digest: March 2021

A list of popular posts and resources shared on Python DS you might have missed! in March 2021. Topics this month include machine learning, deep learning, data manipulation, statistical modelling, IDEs and notebooks and dashboards.

Machine learning

• GPBoost • Combining tree-boosting with Gaussian process and mixed effects models by Fabio Sigrist

• Decision Trees and Random Forests in Python by Nick McCullum

• The Uncanny Dominance of the Python ML Ecosystem by Luke Merrick

• Introduction to scikit-learn (and Course Configurations) by Nick McCullum

• 4 Python AutoML Libraries Every Data Scientist Should Know | Make your life easier by Andre Ye

• Reinforcement Learning: All About Markov Decision Processes by Ayoosh Kathuri

• XGboost Template • This is starter code for a machine learning workflow using the best machine learning model for tabular data by Eric Wheeler

• Multi-dimensional scaling — An illustration of the metric and non-metric MDS on generated noisy data in scikit-learn. by scikit-learn

• network_TDA_tutorial • This repository is dedicated for the tutorial on network and topological neuroscience. by Eduarda Zampieri and Fernando Santos

• msds621 • Course notes for MSDS621 at Univ of San Francisco, introduction to machine learning by Terence Parr

Deep learning

• handson-ml2 • A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2. by Aurélien Geron

• Setting up Python, numpy, and PyTorch natively on Apple M1 by Hendrik Erz

• PyTorch Dataset Class: On the Elegance of Pytorch’s Dataset class by Nelson Gonzabato

• Prediction Intervals for Deep Learning Neural Networks by Machine Learning Mastery

• Spiking Neuronal Networks in Python – Scientific Programming Blog by Daniel Müller-Komorowska

• A tale of two frameworks: PyTorch vs. TensorFlow | Comparing auto-diff and dynamic model sub-classing approaches with PyTorch 1.x and TensorFlow 2.xMedium by Jacopo Mangiavacchi

• A Recipe for Training Neural Networks by Andrej Karpathy

• foolbox • A Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, and JAX by Bethge Lab

Feature selection and engineering

• boruta_py • Python implementations of the Boruta all-relevant feature selection method. by scikit learn

• Splines in Python for Feature Selection and Data Smoothing by Robbie Prior

• feature engine • Feature engineering package with sklearn like functionality by Soledad Galli

Notebooks and IDEs

• Using Python Environments in Visual Studio Code by Visual Studio Code

• Gather: A New Way To Clean Notebooks | Python by Python Visual Studio Code

• Data science notebooks in Review 2020 | Digging into the data to learn more about key trends in Jupyter notebooks. by Deepnote

• itkwidgets • Interactive Jupyter widgets to visualize images, point sets, and meshes in 2D and 3D by Insight Software Consortium

• nb_black • A simple extension for Jupyter Notebook and Jupyter Lab to beautify Python code automatically using black. by Khoa Duong

Visualisation

• Scientific Visualization Using Python by Jessica Nash

• Visualizing the stock market structure — scikit-learn by Gael Varoquaux

• 3D Network Graphs | Python/v3 | Plotly by plotly

• pretty-confusion-matrix • Plot a pretty confusion matrix in Python with MATLAB like style. by Phongsathorn

Statistical modelling

• Prediction Intervals for Gradient Boosting Regression — scikit-learn 1.0.dev0 documentation by scikit-learn

• Linear Regression Analysis with statsmodels in Python by CMDline tips

• Machine Learning, Statistics, and Data Mining for Heliophysics by Téo Bloch, Clare Watt, Mathew Owens, Leland McInnes and Allan R. Macneil

• Introducing PyMC Labs — While My MCMC Gently Samples by Thomas Wiecki

Dashboards and apps

• A high-level app and dashboarding solution for Python by Panel

• Streamlit Firestore. Using Streamlit and Firestore to create a serverless web app with persistent data, written entirely in Python! by Streamlit

Data manipulation & pandas

• 100-pandas-puzzles • 100 data puzzles for pandas, ranging from short and simple to super tricky (60% complete) by Alex Riley

• Pandas: Advanced Aggregation – Brett Romero b Brett Romero

• geopandas • Python tools for geographic data by GeoPandas

• Working with SQL using Python and Pandas by DataQuest

• Pandas: How to Pivot data – Brett Romero by Brett Romero

Tools & utilies

• Using Python’s datatable library seamlessly on Kaggle by Parul Pandey and Rohan Rao

• Ultimate Guide to Web Scraping with Python Part 1: Requests and BeautifulSoup by LearnDataSci

• dagster • A data orchestrator for machine learning, analytics, and ETL. by Dagster

• ocropy • Python-based tools for document analysis and OCR by Ocropus